3D Object Inspection for Segmented Defect Detection and Scoring
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Solution Overview
Problem
Existing methods for inspecting three-dimensional objects, such as pouch cells, are either complex or provide limited quality assessment, failing to effectively detect defects and ensure comprehensive quality evaluation.
Innovation Solution
A method utilizing a matrix camera and area lighting unit to capture image data from three-dimensional objects, processed by a data processing unit with neural networks to segment and analyze different sections of the object, identifying defects and determining a quality score through trained neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a matrix camera captures image data from three-dimensional objects with area lighting, then comprehensive quality assessment is enabled, but the complexity of the inspection device increases
Solution Approach 1:
The patent segments the inspection task by dividing the object surface into multiple sections (upper surface section, lateral surface sections, corner sections) and capturing each section with dedicated lighting and camera arrangements. This segmentation allows comprehensive quality assessment while managing device complexity through modular design of each inspection zone.
Solution Approach 2:
The inspection device is designed with multi-functional capabilities, where the same matrix camera and lighting system can inspect different sections of three-dimensional objects by adjusting capture parameters and processing algorithms. This universality reduces overall device complexity while maintaining comprehensive inspection capabilities.
2Measurement precision
If neural networks are used to analyze image data sections, then defect detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent divides the image data into multiple sections corresponding to different object surfaces (upper, lateral, corner sections) and processes each section with specialized neural network models. This segmentation allows parallel processing and reduces overall processing time while maintaining high accuracy through section-specific analysis.
Solution Approach 2:
The system applies neural network analysis to specific critical sections of the object where defects are most likely to occur, rather than uniformly processing the entire object surface. This partial action approach improves processing efficiency while maintaining detection accuracy for the most important inspection areas.
3Measurement precision
If image data is segmented into multiple sections, then quality assessment comprehensiveness is improved, but data processing complexity increases
Solution Approach 1:
The patent implements segmentation of image data into distinct sections (upper surface, lateral surfaces, corner sections) with dedicated processing algorithms for each section. This structured segmentation approach improves comprehensiveness while managing processing complexity through standardized processing pipelines for each section type.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables fast and accurate detection of defects in three-dimensional objects, providing a comprehensive quality assessment by separately analyzing flat and lateral surface sections, enhancing defect identification and quality evaluation efficiency.
Implementation Method 1
image data captured matrix-wise by means of a matrix camera is generated for each object from an area lighting unit's light reflected from the top side
Data Source
AI summary
A method and device for inspecting three-dimensional objects, wherein each object includes a top side composed of at least one upper surface section and a plurality of lateral surface sections which extend obliquely, parallel or perpendicular to the at least one upper surface section or represent corner sections, and a bottom side. For each object, image data captured matrix-wise by a matrix camera is generated from an area lighting unit's light reflected from the top side in a rest state of the object and transmitted to a data processing unit, wherein the image data captured matrix-wise comprises light reflected from the lateral surface portions. The image data captured matrix-wise is further processed as a first overall matrix by the data processing unit, which performs segmentation of the first overall matrix and identifying a defect type of a detected defect and/or a severity of a detected defect and/or determining a quality score.